Quantitative texture measurement apparatus and method

ABSTRACT

A photo acoustic non-destructive measurement apparatus and method for quantitatively measuring texture of a food snack is disclosed. The apparatus includes a laser generating tool, an acoustic capturing device, and a data processing unit. The laser generating tool directs a laser towards a food snack placed on a surface and creates pressure waves that propagate through the air and produce an acoustic signal. The acoustic capturing device records and forwards the signal to a data processing unit. The data processing unit further comprises a digital signal processing module that processes the received acoustic signal. A statistical processing module further filters the acoustic signal from the data processing unit and generates a quantitative acoustic model for texture attributes such as hardness and fracturability. The quantitative model is correlated with a qualitative texture measurement from a descriptive expert panel. Texture of food snacks are quantitatively measured with the quantitative acoustic model.

FIELD OF THE INVENTION

The present invention relates to a quantitative measurement of texturefor food products using non-invasive photo acoustic techniques.

PRIOR ART AND BACKGROUND OF THE INVENTION Prior Art Background

Texture is one of the most important sensory characteristics thatdetermine consumer preference for food products and is usually assessedby sensory evaluation. However, sensory evaluation is time-consuming andexpensive, and therefore, reliable and practical instrumental methodsare needed to accurately predict sensory texture attributes and otherfood snack properties.

When a food snack such as potato chip is manufactured, texturalproperties are dependent on raw material characteristics (i.e. lowsolids or high solids potatoes) and the processing conditions that theraw material undergoes such as temperature profile, slice thickness, aswell as finished product characteristics such as moisture, oil content,etc.

The crispiness, softness and/or crunchiness of a potato chip are just afew examples of texture and mouthfeel characteristics that make foodappealing and satisfying to consumers. Texture is one of the majorcriteria which consumers use to judge the quality and freshness of manyfoods. When a food produces a physical sensation in the mouth (hard,soft, crisp, moist, dry), the consumer has a basis for determining thefood's quality (fresh, stale, tender, ripe)

A major challenge is how to accurately and objectively measure textureand mouthfeel. Texture is a composite property related to a number ofphysical properties (e.g., hardness and fracturability), and therelationship is complex. Texture or mouthfeel cannot be quantitativelymeasured in a single value obtained from an instrument. Mouthfeel ishard to define as it involves food's entire physical and chemicalinteraction in the mouth—from initial perception on the palate, to firstbite, through mastication and finally, the act of swallowing. There is aneed to quantitatively measure the food interaction in the mouth.

A problem with hardness is that their correlations with sensory testsare not always as high as expected. In many instances, the metric ofpeak force exerted on a potato chip does not adequately replicate thetexture experienced by consumers. Therefore, consumers' judgments ofhardness can be more nuanced than a simple peak force metric from adestructive analytical test.

Presently, there is no good correlation of any type between instrumentreadings and taste panel scores. The issue is that no instrument iscapable of manipulating a food product precisely the same way as thehuman mouth during mastication. For example, an instrument may compressa food product between two plates, while a human would be biting downwith incisors. Therefore, there is a need for a quantitative texturemeasurement that has a good correlation with a qualitative measurementfrom an expert panel.

Prior Art Texture Measurement System

The Universal TA-XT2 Texture Analyzer from Texture Technologies Corp.,can perform a complete TPA calculation and comes with multiple standardprobes, including various sizes of needles, cones, cylinders, punches,knives and balls. FIG. 1. Illustrates a prior art system for measuringtexture attributes such as hardness and fracturability with a TA-XT2Texture Analyzer. The system includes a probe (0101) that exerts a forceon a food snack such as a potato chip and measure the amount of forcerequired to break the chip. Hardness may be measured as a force requiredto deform the product to given distance, i.e., force to compress betweenmolars, bite through with incisors, compress between tongue and palate.

Prior Art Texture Measurement Method

As generally shown in FIG. 2, a prior art texture measurement methodassociated with the prior art system may include the steps comprising:

-   -   (1) placing a food snack on a surface (0201);    -   (2) with a probe, exerting a force and break/deform the food        snack (0202);    -   (3) generating an acoustic signal from the food snack or        measuring the force exerted (0203);    -    Force exerted may depend on the shape of the food snack. For        example, a U shaped food snack or a curvy shaped food snack may        be placed in either direction and the force exerted to break the        food snack may be different. Therefore, there is a need for a        shape independent quantitative texture measurement.    -   (4) capturing the acoustic signal with an acoustic capturing        device or record the force required to break the food snack        (0204);    -    acoustic signal is captured for a period of time at preset        frequencies and the signal is plotted as Time (seconds) vs.        Intensity (dB). There is a need to measure acoustic signal in a        wide range of frequencies. Prior art system provide for weaker        correlation when 4 Khz and 8 KHZ are used as the sound envelope        and ignore all other frequencies.    -   (5) generating a texture model from the acoustic signal (0205);        and    -    A model for texture attributes such as hardness and        fracturability is developed from the Time vs. Intensity plot for        the food snack. Alternatively, a model from measured force may        also be used to develop a model.    -   (6) measuring the texture attribute of the food snack from the        texture model.    -    Texture attributes of a food snack is measured from the model        developed in step (0205). The texture attributes are correlated        to a qualitative texture attributes number from an expert panel        as described below in FIG. 3.

Prior Art Texture Correlation Method

As generally shown in FIG. 3, a prior art texture correlation method mayinclude the steps comprising:

-   -   (1) shipping food snack samples to an expert panel (0301);    -    The shipping of the food snack samples may take time and the        food snack may undergo texture change during the shipping        process. Therefore, there is a need to limit the number of times        food snacks are shipped the expert panel.    -   (2) Qualitatively analyzing the food snack samples (0302);    -    The process starts with a well-trained sensory panel to carry        out a meaningful texture profile analysis, a panel of judges        needs to have prior rating knowledge of the texture        classification system, the use of standard rating scales and the        correct procedures related to the mechanics of testing. Panelist        training starts with a clear definition of each attribute.        Furthermore, the techniques used to evaluate the food product        should be explicitly specified, explaining how the food product        is placed in the mouth, whether it is acted upon by the teeth        (and which teeth) or by the tongue and what particular sensation        is to be evaluated. Panelists are given reference standards for        evaluation so they can practice their sensory evaluation        techniques and the use of scales. Hardness and fracturability        are usually considered to be the most important texture        attribute. Presently there is no good correlation of any type        between instrument readings and taste panel scores. Presently        there are no instruments capable of manipulating a food product        precisely the same way as the human mouth during mastication.        For example, an instrument may compress a food product between        two plates, while a human would be biting down with incisors. In        fact, what an instrument measures may not relate at all to what        the consumer perceives. Therefore, there is a need to have a        system that can quantitatively measure texture attributes and        correlate to the taste panel scores.    -   (3) assigning a descriptive panel number for the texture        attributes of the food snack sample (0303);    -    A organoleptic sensory evaluation is performed in which the        trained panelists assign intensity levels on various        descriptors/texture attributes. For example, for evaluating the        potato chips, hardness may be considered one important        attribute. In this case, panelists assign a hardness score based        on a scale, where 1 equals extremely soft and 15 equals        extremely hard. The panelists may rate the hardness of potato        chip samples A, B and C's. After taste paneling is complete,        instrument readings of the food product are made as described        below in step (0304).    -   (4) Measure texture attributes using an invasive analytical        method (0304);    -    There is a need that the instrumental technique selected        duplicates as closely as possible how the mouth manipulates the        particular food product. The instrument should apply the same        amount of force in the same direction and at the same rate as        the mouth and teeth do during mastication. The instrument may        record acoustic signals for a period of time and generate a        model. However, current instruments are limited by recording        acoustics at discrete frequencies such as between 4000 and 8000        kHz. Therefore, there is a need for recording sound in a wider        frequency range.    -   (5) Correlate the analytical and the qualitative texture        attributes (0305); and    -    Statistically correlate between sensory data (descriptive panel        number) and instrumental measurements. Currently, correlation        based on Intensity vs. Time measurements, generate a weak        correlation statistically. For example, prior art adjusted R²        correlation numbers are in the range of 0.5-0.65 with the time        domain acoustic model. Therefore, there is a need for a strong        correlation between descriptive panel number and the analytical        model.    -   (6) Generating a correlation model (0306).

Consequently, there is a need for a non-invasive quantitative texturemeasurement that accomplishes the following objectives:

-   -   Provide a quantitative method to measure finished product        attributes such as oil content, moisture, slice thickness, and        salt content.    -   Provide for quantitative analytical measurement of the textural        attributes such as hardness, fracturability, crispiness, and        surface oiliness.    -   Provide for analyzing frequency domain data to accurately model        the texture attributes.    -   Provide for acoustic signal capture in a broad frequency range        from 0 to 200 KHz    -   Provide for shape independent quantitative test for texture        measurement.    -   Provide for a non-destructive quantitative measurement of        texture of a food snack.    -   Provide for quantitative measurement of texture with minimum        samples with greater accuracy and reliability.    -   Provide for a less expensive quantitative texture measurement        test.    -   Provide for instant results of the quantitative measurement.    -   Provide for an accurate model with good correlation with an R²        greater than 0.9.    -   Provide for high resolution texture measurement with better than        5% accuracy.    -   Provide for repeatable and reproducible quantitative        measurements of food snacks.

While these objectives should not be understood to limit the teachingsof the present invention, in general these objectives are achieved inpart or in whole by the disclosed invention that is discussed in thefollowing sections. One skilled in the art will no doubt be able toselect aspects of the present invention as disclosed to affect anycombination of the objectives described above.

BRIEF SUMMARY OF THE INVENTION

The present invention in various embodiments addresses one or more ofthe above objectives in the following manner. The texture measuringapparatus includes an energy excitation tool, an acoustic capturingdevice, and a data processing unit. The energy excitation tool directs alaser towards a food snack placed on a surface and creates rapidexpansion of the material which in results in creation of air pressurewaves that propagate through the air and produce an acoustic signal. Theacoustic capturing device records and forwards the signal to a dataprocessing unit. The data processing unit further comprises a digitalsignal processing module that smoothens, transforms and filters thereceived acoustic signal. A statistical processing module furtherfilters the acoustic signal from the data processing unit and generatesa quantitative acoustic model for texture attributes such as hardness,fracturability, crispiness, etc. The quantitative model is correlatedwith a qualitative texture measurement from a descriptive expert panel.Texture of food snacks are quantitatively measured with the quantitativeacoustic model with the apparatus.

The present invention system may be utilized in the context of method ofquantitatively measuring texture of a snack food, the method comprisesthe steps of:

-   -   (1) placing a snack food on a moving or a non-movable surface;    -   (2) directing electromagnetic wave (energy) such as a laser to        strike the food snack;    -   (3) generating an acoustic signal from the snack food;    -   (4) capturing the acoustic signal with an acoustic capturing        device;    -   (5) forwarding the acoustic signal to a data processing unit;        and    -   (6) measuring the texture of the snack food with texture        attributes from the texture model.

Integration of this and other preferred exemplary embodiment methods inconjunction with a variety of preferred exemplary embodiment systemsdescribed herein in anticipation by the overall scope of the presentinvention.

BRIEF DESCRIPTION OF THE DRAWINGS

For a fuller understanding of the advantages provided by the invention,reference should be made to the following detailed description togetherwith the accompanying drawings wherein:

FIG. 1 is a prior art invasive system for measuring texture in foodproducts.

FIG. 2 is a prior art chart for measuring texture with acoustic signals.

FIG. 3 is a prior art method for correlating texture measurements.

FIG. 4 is a system for quantitative measurement of texture attributesaccording to an exemplary embodiment of the present invention.

FIG. 5 is an excitation tool that directs energy on a food productaccording to an exemplary embodiment of the present invention.

FIG. 6 is an acoustic capturing unit that captures an acoustic signalaccording to an exemplary embodiment of the present invention.

FIG. 6a is a texture measuring apparatus comprising a parabolic dishshaped housing and an acoustic capturing device positioned within thedish, according to an exemplary embodiment of the present invention.

FIG. 7 is a data processing unit according to an exemplary embodiment ofthe present invention.

FIG. 8 is a digital signal processing unit according to an exemplaryembodiment of the present invention.

FIG. 9 is a statistical processing unit according to an exemplaryembodiment of the present invention.

FIG. 10 is a flow chart method for quantitative measurement of textureaccording to an exemplary embodiment of the present invention.

FIG. 11 is an exemplary flow chart method for quantitative correlationof texture according to a preferred embodiment of the present invention.

FIG. 12 is an exemplary flow chart method for quantitative texture modeldevelopment according to a preferred embodiment of the presentinvention.

FIG. 13 is an exemplary flow chart method for photo acoustic signalgeneration according to a preferred embodiment of the present invention.

FIG. 14 is an exemplary flow chart method for acoustic signal processingaccording to a preferred embodiment of the present invention.

FIG. 15 is an exemplary flow chart method for acoustic statisticalprocessing according to a preferred embodiment of the present invention.

FIG. 16 is an exemplary food snack fingerprinting method according to apreferred exemplary embodiment.

FIG. 17 is an exemplary food snack fingerprinting matching tableaccording to a preferred exemplary embodiment.

FIG. 18 is an exemplary acoustic signal time domain to frequency domaintransformation chart according to a preferred embodiment of the presentinvention.

FIG. 19 is an exemplary texture attribute (hardness) vs. relevantfrequencies chart according to a preferred embodiment of the presentinvention.

FIG. 20 is an exemplary texture attribute (fracturability) vs. relevantfrequencies chart according to a preferred embodiment of the presentinvention.

FIG. 21 is another exemplary texture attribute (hardness) vs. relevantfrequencies chart according to a preferred embodiment of the presentinvention.

DESCRIPTION OF THE PRESENTLY EXEMPLARY EMBODIMENTS

While this invention is susceptible of embodiment in many differentforms, there is shown in the drawings and will herein be described indetailed preferred embodiment of the invention with the understandingthat the present disclosure is to be considered as an exemplification ofthe principles of the invention and is not intended to limit the broadaspect of the invention to the embodiment illustrated.

The numerous innovative teachings of the present application will bedescribed with particular reference to the presently exemplaryembodiment, wherein these innovative teachings are advantageouslyapplied to quantitative measurement of texture attributes for foodsnacks apparatus and method. However, it should be understood that thisembodiment is only one example of the many advantageous uses of theinnovative teachings herein. In general, statements made in thespecification of the present application do not necessarily limit any ofthe various claimed inventions. Moreover, some statements may apply tosome inventive features but not to others.

The term “texture” as used herein is defined a composite propertyrelated to a number of physical properties such as hardness,fracturability, tooth-pack, roughness of mass, moistness of mass,residual greasiness, surface roughness, and surface oiliness. It shouldbe noted that the term “texture” and “texture attribute” is usedinterchangeably to indicate one or more properties of texture. It shouldbe noted that the terms “descriptive panel number”, “taste panel score”,“qualitative texture number” and “taste panel number” are usedinter-changeably to indicate a qualitative measurement of texturemeasurements by an expert panel. It should be noted that the terms“photo acoustic model” “acoustic model” “acoustic texture model”“quantitative texture attribute model” are used inter-changeably toindicate a quantitative model for a texture attribute of a food snack.

Exemplary Embodiment System for Quantitative Measurement of TextureAttributes (0400-0900)

One aspect of the present invention provides a method to quantitativelymeasure the texture attributes of food snacks. Another aspect of thepresent invention involves correlating the quantitative textureattribute measurement to a qualitatively measured texture attribute byan expert panel. The present invention is also directed towardsdeveloping a texture attribute model based on relevant frequencies in acaptured acoustic signal. According to yet another aspect of the presentinvention, food snacks are identified (“food finger printing”) based onphoto acoustic quantitative food snack property measurement.

Applicants herein have created a system that comprises an energyexcitation tool for directing energy towards a food snack, an acousticcapturing device for recording/capturing an acoustic signal from thefood snack and a data processing unit that processes the capturedacoustic signal and generates a texture attribute model. In oneembodiment, the energy excitation tool is a laser generating tool thatis configured to generate a laser. There are a number of embodiments ofthis invention which fall within the scope of the invention in itsbroadest sense.

Exemplary Embodiment Texture Measurement Tool (0400)

The present invention may be seen in more detail as generallyillustrated in FIG. 4, wherein an exemplary texture measurement tool(0400) comprises a housing, an energy excitation tool (0401) that isattached to the housing and positioned to direct electromagnetic wave(“energy”) such as a laser (0407) towards a food snack (0409) placed ona food staging station (0405). According to a preferred exemplaryembodiment, the food snack is a starch based food snack. According toanother preferred exemplary embodiment, the food snack is potato chips.The food staging station may be a movable or a non-movable surface.According to a preferred exemplary embodiment, the energy excitationtool is a laser generating unit that generates lasers. It should benoted that any tool that can generate excitation on a food substrate maybe used as an energy excitation tool. The staging station (0405) may bea flat surface that is used for developing an acoustic model. Thestaging station (0405) may be a conveyor belt carrying the food snackswhen texture is measured in a manufacturing process on-line. Accordingto an exemplary embodiment, an acoustic capturing device (0403) may bepositioned to record/capture an acoustic signal (0406) from the foodsnack (0409). The acoustic capturing device (0403) may be incommunication with a data processing unit (DPU) (0404) via a cable(0402) or wirelessly. The acoustic capturing device may capture theacoustic signal across a wide range of frequencies 0 Khz to 500 Khz.Additionally, the acoustic capturing device (0403) may be placed at anangle directly above the food snack (0409). According to a preferredexemplary embodiment, the acoustic capturing device captures acousticsignals in a unidirectional manner. The acoustic capturing device may bein communication with a data processing unit. According to anotherpreferred exemplary embodiment, the acoustic capturing device capturesacoustic signals in omnidirectional manner. According to a preferredexemplary embodiment, the acoustic capturing device is a wirelessmicrophone that contains a radio transmitter. In a preferred exemplaryembodiment, the acoustic capturing device is a dynamic microphone. Inanother preferred exemplary embodiment, the acoustic capturing device isa fiber optic microphone. The acoustic capturing device (0403) may beplaced at a pre-determined distance and a pre-determined angle from thefood snack (0409). The pre-determined distance may be chosen such thatit produces maximum energy density from the food snack. The distance(0408) from the bottom of energy excitation tool (0401) to the top ofthe staging station (0405) is selected so that the energy beam (laser)is safe within the manufacturing environment. According to a preferredexemplary embodiment, the distance from the

The acoustic capturing device (0403) may be connected physically with aconducting cable to the DPU (0404) via an input-output module in the DPU(0404). In an alternate arrangement, the acoustic capturing device(0403) may forward an acoustic signal to the input-output module in theDPU (0404) wirelessly. The wireless protocol may use standard protocolssuch as WIFI or Bluetooth. In an exemplary embodiment, the acousticcapturing device (0403) may be remotely located and the acoustic signalmay be forwarded wirelessly to the DPU (0404) with a protocol such asLTE, 3G and/or 4G. In another exemplary embodiment, the remotely locatedDPU (0404) may be connected to the acoustic capturing device (0403) withwired protocol such as Ethernet.

The energy excitation tool (0401) is positioned to direct energy towardsa food snack (0409). It should be noted that the angle of directing asshown is for illustration purposes only. The angle of directing theenergy may be configured to produce an optimal excitation of the foodsnack such that an acoustic capture device (0403) may capture a completeacoustic signal after the excitation tool directs energy towards thefood snack. The acoustic signal may then be captured for a period oftime. The acoustic signal may be represented as Intensity (dB) vs. Time(secs). According to a preferred exemplary embodiment, the acousticsignal is captured for 1 sec to 5 minutes. According to yet anotherpreferred exemplary embodiment, the acoustic signal from the food snackis captured for 2 sec. According to a more preferred exemplaryembodiment, the acoustic signal from the food snack is captured for 1sec. According to a most preferred exemplary embodiment, the acousticsignal from the food snack is captured for 10 sec.

According to a preferred exemplary embodiment, the energy excitationtool directs energy towards the food snack for a pulse duration orfiring time of 5 nanoseconds to 5 minutes. According to yet anotherpreferred exemplary embodiment, the energy excitation tool directsenergy towards the food snack for 1 nanosecond. According to a morepreferred exemplary embodiment, the energy excitation tool directsenergy towards the food snack for 1 minute. According to a mostpreferred exemplary embodiment, the energy excitation tool directsenergy towards the food snack for 9-12 nanoseconds.

Exemplary Energy Excitation Tool (0500)

As generally illustrated in FIG. 5 (0500), an exemplary energyexcitation tool (0500) that is similar to (0401) in FIG. 4 (0400)comprises an energy generating unit (0504) that is mounted within anenergy enclosure (0505). The energy generating unit (0504) may generatean electromagnetic wave that may excite molecules from a food substratecausing the molecules to gain heat energy and vibrate producing a sound.The electromagnetic wave may comprise a wavelength in the range of 512nm to 2048 nm. A more preferred range of the electromagnetic wave maycomprise a wavelength in the range of 470 nm to 1 mm. The energygenerating unit (0504) may excite molecules from a food substratecausing the molecules to vibrate a produce sound. Excitation may bedefined as an elevation in energy level above an arbitrary baselineenergy state. When molecules are excited the thermal expansivity may berelated to the type and density of material in accordance with thefollowing equation. Texture may be indirectly related to thermalexpansivity and therefore texture is indirectly related to the type anddensity of the material.

$\alpha_{v} = {{\frac{1}{V}\frac{\partial(V)}{\partial T}} = {{\frac{1}{\frac{1}{\rho}}\frac{\partial\left( \frac{1}{\rho} \right)}{\partial T}} = {{\rho\frac{\partial\left( \rho^{- 1} \right)}{\partial T}} = {{{- \frac{\rho}{\rho^{2}}}\frac{\partial(\rho)}{\partial T}} = {{{- \frac{1}{\rho}}\frac{\partial(\rho)}{\partial T}} - \frac{\partial{\ln(\rho)}}{\partial T}}}}}}$Thermal expansivity=function (material, density)Texture=function (material, density)

A specific technical definition for energy level is often associatedwith an atom being raised to an excited state. The energy excitationtool, in a preferred exemplary embodiment, is a laser generating toolthat produces a very narrow, highly concentrated beam of light. A laseris a device that emits light through a process of optical amplificationbased on the stimulated emission of electromagnetic radiation. Spatialcoherence in the laser allows a laser to be focused to a tight spot.Spatial coherence also allows a laser beam to stay narrow over greatdistances (collimation). Lasers can also have high temporal coherence,which allows them to emit light with a very narrow spectrum, i.e., theycan emit a single color of light. The energy generating unit (0504)(“laser generating unit”) may include a gain medium, laser pumpingenergy, high reflector, output coupler and a laser beam. The laser beam(0502) may travel through a hollow tube (0503) and strike a mirror(0501). The hollow tube (0503) may be held by a metallic arm (0512) thatis mechanically connected to the energy enclosure (0505). In a preferredexemplary embodiment, the laser beam may travel without the need for ahollow tube. The metallic arm may be made of a metal that may carry theweight of the hollow tube (0503) and the housing (0506). The laser maycontain additional elements that affect properties of the emitted light,such as the polarization, wavelength, spot size, divergence, and shapeof the beam.

The mirror (0501) reflects the laser beam (0502) towards a food snacksubstrate positioned on a surface. According to a preferred exemplaryembodiment, the mirror is angled between 1 degree and 89 degrees to thevertical. According to a most preferred exemplary embodiment, the mirroris angled at 45 degrees to the vertical. Any combination of multiplemirrors, multiple lenses, and expanders may be used to produce aconsistent spot size laser that strikes the food snack. The laser beamfrom the laser generating unit may be redirected, expanded and focusedas the beam passes through a combination of mirrors and lenses. Itshould be noted that even though a single mirror and single lens areillustrated in FIG. 5, it should not be construed as a limitation andany combination of the mirrors, lenses and expanders may be used toproduce a constant spot size laser beam. The reflected laser beam (0509)passes through a narrow window (0511) in a housing (0506). An acousticdevice enclosure (0507) for housing an acoustic capturing device may bemounted in the housing (0506). It should be noted that the enclosure(0506) as illustrated in FIG. 5 (0500) is shaped as rectangular, howeverany shape may be used for the enclosure that is capable of beingacoustically insulated and human safe. According to a preferredexemplary embodiment, the housing (0506) may be cylindrical, cubical,conical, spherical or triangular prism shaped. Similarly, acousticdevice enclosure (0507) may be shaped as rectangular prism, cylindrical,cubical, conical, spherical, or triangular prism. The acoustic deviceenclosure (0507) may house an acoustic device such as a microphone. Theacoustic device enclosure (0507) may also maintain a positive airpressure in order to ensure a particulate free environment within theenclosure (0507). The positive air pressure may be maintained by blowingair through the enclosure with an air pump. According to a preferredexemplary embodiment, the narrow window (0511) may be made out asapphire material or fused silica. Any translucent window that separatesthe laser beam from the food product may be used as the narrow window.According to another preferred exemplary embodiment, the narrow window(0511) is aligned such that the laser beam (0509) is within +−1 degreeto a desired direction. The desired direction may be vertical or at anangle to a vertical plane. A laser level sensor (0510) is positionedwithin the housing (0506) to sense the level of the food from thesurface. The laser sensor (0501) may prevent humans from undesired entryinto the housing (0506). For example, if the laser sensor detects anobject or a human hand over the food snack, it may automatically shutoff the laser and prevent from exposing the human to the laser.According to a preferred exemplary embodiment, the laser level providesfor a human safe laser environment. According to another preferredexemplary embodiment, the laser level detects a food snack within +−2inches from a staging surface. A temp sensor (0511) may be positionedwithin the housing (0506) to measure temperature. According to apreferred exemplary embodiment, a texture attribute measurement of thefood product may be compensated for temperature fluctuations of the foodproduct.

The laser beam from the laser generator may also be directed via fiberoptic cable to the product bed, with any number of focusing andexpanding optics coupled with the fiber optic cable in between the laserand the product. The fiber optic cable does not need to be parallel tothe beam path, aside from end at which the laser beam enters the fiberoptic cables.

Exemplary Energy Excitation Tool and Exemplary Acoustic Capturing Device(0600)

As generally illustrated in FIG. 6, a before and after energy excitationfrom an energy excitation tool is shown. The energy excitation tool(0601) is positioned to direct energy (“electromagnetic wave”) towards afood snack (0602). It should be noted that the angle of directing asshown is for illustration purposes only. The angle of directing theenergy may be configured to produce an optimal excitation of the foodsnack such that an acoustic capture device (0603) may capture a completeacoustic signal after the excitation tool directs energy towards thefood snack. The acoustic signal may then be captured for a period oftime. The acoustic signal may be represented as Intensity (dB) vs. Time(secs or micro secs). According to a preferred exemplary embodiment, theacoustic signal is captured for 1 sec to 3 minutes. According to yetanother preferred exemplary embodiment, the acoustic signal from thefood snack is captured for 10 sec. According to a more preferredexemplary embodiment, the acoustic signal from the food snack iscaptured for 1 sec. According to a most preferred exemplary embodiment,the acoustic signal from the food snack is captured for 10 seconds.

According to a preferred exemplary embodiment, the energy excitationtool directs energy towards the food snack for 1 sec to 3 minutes.According to yet another preferred exemplary embodiment, the energyexcitation tool directs energy towards the food snack for 1 microsecond. According to a more preferred exemplary embodiment, the energyexcitation tool directs energy towards the food snack for 1 minute.According to a most preferred exemplary embodiment, the energyexcitation tool directs energy towards the food snack for 10 seconds.

According to a preferred exemplary embodiment, fluence (energy per unitarea) at the product bed is between 15 mJ/mm2 and 700 mJ/mm2. Accordingto a more preferred exemplary embodiment, fluence at the product bed isbetween 62.5 mJ/mm² and 594.5 mJ/mm². According to a yet anotherpreferred exemplary embodiment, fluence at the product bed is between300 mJ/mm² and 350 mJ/mm². According to a most preferred exemplaryembodiment, fluence at the product bed is 311 mJ/mm2.

In order to achieve the most optimal energy density, the diameter of thelaser beam may be customized from the laser generator. According to apreferred exemplary embodiment, the laser beam diameter ranges from 100micrometers to 400 micrometers. According to a preferred exemplaryembodiment, the laser beam diameter ranges from 250 micrometers to 350micrometers. According to a preferred exemplary embodiment, the laserbeam diameter is 300 micrometers. The diameter of the laser beam may beadjusted to ensure that maximum excitation energy density is achievedwithin a four inch window (+/−2 inches from center point). The point ofimpact of the laser beam on the product bed should ideally be at thebeam's focal point (which is the point of highest energy density), orwithin +/−2 inches of the focal point according to a preferred exemplaryembodiment. The apparatus may use mirrors and focusing lenses with anAnti-Reflective (AR) coating for 1064 nm wavelengths. An example of thebeam and focusing mirror arrangement may be a beam that originates atthe laser generator, strikes a turning mirror positioned 702 mm away,and reflects 400 mm downward to pass through a focusing optic, which isalso Anti-Reflective coated for 1064 nm wavelengths. The beam may thenpass through a final window that is designed to seal the optics awayfrom the external environment and prevent any oil/debris build-up fromforming on the optics. According to a preferred exemplary embodiment, apreferred spot size is achieved at 200 mm-600 mm away from the focusingoptic. According to more a preferred exemplary embodiment, a preferredspot size is achieved at 300 mm-500 mm away from the focusing optic.According to most a preferred exemplary embodiment, a preferred spotsize is achieved at 400 mm from the focusing optic.

The acoustic capturing device such as a microphone may be directionallypointed at the point of beam impact at the product bed and positionedsuch that it is no more than 2 feet away. According to a preferredexemplary embodiment, the acoustic capturing device is positioned inbetween 1 inch and 2 feet from the point of beam impact on the foodproduct. According to a preferred exemplary embodiment, the acousticcapturing device is positioned in between 1 inch and 1 foot from thepoint of beam impact on the food product. According to a preferredexemplary embodiment, the acoustic capturing device is positioned inbetween 1 feet and 2 feet away from the point of beam impact on the foodproduct.

According to another preferred exemplary embodiment, the housing may beshaped cylindrical. According to yet another preferred exemplaryembodiment, the housing may be shaped as a parabolic dish. As generallyillustrated in FIG. 6a (0610), a laser beam generator (0618) housedwithin an energy enclosure (0615) generates a laser beam (0619). Thelaser beam may be reflected from a mirror (0611) and thereafter strike afood product (0614) that may be passing on a movable surface such as aconveyor belt (0617). When the laser beam strikes the food product, anacoustic signal may be generated. An acoustic capturing device (0612)such as a microphone may be positioned within a housing (0616) tocapture an optical signal (0613) with maximum energy density. Theacoustic capturing device (0612) such as a microphone may be centered ata parabolic dish, which would direct the acoustic signals to themicrophone. A temp sensor may be positioned within the housing (0616) tomeasure temperature of the food product. According to a preferredexemplary embodiment, a texture attribute measurement of the foodproduct may be compensated for temperature fluctuations of the foodproduct.

Exemplary Data Processing Unit (0700)

As generally illustrated in FIG. 7 (0700), a data processing unit (DPU)(0701) comprises a control unit, a display unit, a processing unit andan input output module. The control unit may further comprise amicrocontroller (0707), a logic controller (0706), and a networkcontroller (0705). The display unit may be connected to the control unitvia a host bus. The display unit may further comprise a display terminal(0708) that is configured to display a graphical user interface (GUI)(0709). The GUI (0709) may be navigated with a pointing device orthrough a keyboard connected to the DPU. The GUI (0709) may be used toinput parameters such as food snack specific frequencies, acousticcapture time, acoustic capture frequency range

The processing unit may include a digital signal processing unit (0703)and a statistical processing unit (0704). The digital signal processingunit (0703) may get input from an input-output module (0702). Thestatistical processing unit (0704) may receive input from the digitalprocessing unit (0703) and further process the input to find relevantfrequencies for generating a quantitative acoustic model for a foodsnack. When an acoustic capturing device captures an acoustic signal,the signal may be forwarded to the DPU (0701) via the input-outputmodule (0702). The input output module (0702) may further comprise acustomized hardware such an analog to digital convertor (ADC) forcapturing and processing a captured acoustic signal. The acoustic signalmay be forwarded to the DPU using a wired or a wireless connection. Theconnection protocol and connecting conducting wires may be chosen suchthat there is minimum loss of signal and the signal to noise ratio isacceptable for further processing. A general purpose bus may carry datato and from different modules of the DPU (0701). It should be noted thatthe operation of the bus is beyond the scope of this invention.

The microcontroller (0707) may perform instructions from a memory or aROM (0710). The instruction set of the microcontroller may beimplemented to process the data of the acoustic signal. A custominstruction set may also be used by the microcontroller to prioritizeand expedite the processing of the acoustic signal in real time during amanufacturing operation. The customization of the instruction set isbeyond the scope of this invention. The logic controller may performoperations such as sequencing, prioritization and automation of tasks.The logic controller may also oversee the hand shake protocol for thebus interface. According to an exemplary embodiment, the logiccontroller controls the logic for identifying relevant frequencies in anacoustic signal. The logic controller may comprise a matching modulethat contains predefined frequencies for a plurality of food snacks. Thelogic controller may subsequently match the captured frequencies in theacoustic signal and quickly determine the texture of the food snack andthe quality of the texture. For example, the matching module may includespecific frequencies such as 14000 Hz and 75000 Hz. When a recordedacoustic signal comprises the frequencies 14000 Hz or 75000 Hz, then thelogic controller may determine a match and alert the microcontrollerwith an interrupt signal. The microcontroller may then display thetexture information on the display (0708) via GUI (0709). The logiccontroller may further continuously monitor the state of input devicesand make decisions based upon a custom program to control the state ofoutput devices.

Exemplary Digital Signal Processing Module (0800)

Similar to the digital signal processing unit (0703) shown in FIG. 7(0700), a digital signal processing unit (DSP) (0800) is generallyillustrated in FIG. 8 (0800). The DSP (0800) may further comprise asmoothing module (0801), a data transformation module (0802), a signalto noise enhancing module (0803) and a normalization module (0804).

According to an exemplary embodiment, the acoustic smoothing module(0801) receives input from an input-module in a data processing unit andsmoothens the received raw acoustic signal. Acoustic signals areinherently noisy and the data is discrete. The acoustic signals may berepresented as Intensity (dB) vs. Time (secs or micro seconds). The datais made continuous by applying a windowing function to the discretedata. Windowing functions that may be applied to the discrete data mayinclude Barlett, Blackmon, FlatTop, Hanning, Hamming, Kaiser-Bessel,Turkey and Welch windowing functions. A smoothing window with goodfrequency resolution and low spectral leakage for a random signal typemay be chosen to smoothen the data. It should be noted that any commonlyknown windowing function may be applied to a raw acoustic signal tosmoothen and interpolate the raw acoustic data.

The smoothened acoustic signal from the smoothing module (0801) may beforwarded to a data transformation module (0802). The datatransformation module (0802) may transform the acoustic signalrepresented in time domain as Intensity (dB) vs. Time (secs) tofrequency domain as Intensity (dB) vs. Frequency (Hz) as generally shownin FIG. 18 (1800). According to a preferred exemplary embodiment, thetransformation of acoustic signal from a time domain representation to afrequency domain representation provides for accurately correlatingtexture attributes to the pertinent frequencies of a food snack.Combining multiple acoustic waves produces a complex pattern in the timedomain, but the transformed signal using FFT clearly shows as consistingalmost entirely of distinct frequencies. According to most preferredexemplary embodiment, a fast fourier transformation (FFT) technique maybe used to transform the acoustic signal from a time domainrepresentation to a frequency domain representation. An example of thetransformation may be generally seen in FIG. 18 (1800).

The transformed frequency signal from the transformation module may benoisy. A signal to noise enhancement module (0803) may receive thetransformed signal from the data transform module (0802) and enhance thesignal-to-noise ratio of the signal for further processing. A techniquefor smoothing the data to increase the signal-to-noise ratio withoutgreatly distorting the signal may be used. A process such as convolutionmay also be used to increase the signal-to-noise ratio. The convolutionprocess may fit successive sub-sets of adjacent data points with alow-degree polynomial by the method of linear least squares.Normalization module (0804) may receive the enhanced signal-to-noisefrequency domain signal from the signal to noise enhancement module(0803).

The DSP (0800) may also identify pertinent frequencies and associatedintensities from the enhanced signal-to-noise frequency domain signaland store the information in a database. A texture attribute computingunit (0712) in the DPU (0701) may further retrieve the stored frequencyand intensity information to compute a texture attribute of a foodsnack. After a photo acoustic model has been developed, the textureattribute computing unit (0712) may store coefficients for differentfood snacks. The texture attribute computing unit (0712) may thenretrieve the stored coefficients and the stores frequency and intensityinformation to compute a texture attribute measurement or to fingerprinta food snack.

Exemplary Statistical Processing Unit (0900)

Similar to the statistical processing unit (0704) shown in FIG. 7(0700), a statistical processing unit (SPU) (0900) is generallyillustrated in FIG. 9. The SPU (0900) may further comprise adimensionality regression module (0901), a variance inflation factormodule (0902), a principal component analysis module (0903), and asubset regression module (0904).

The smoothened, transformed and normalized signal from the digitalsignal processing unit (0703) is forwarded to SPU (0704) for developingtexture attribute model with good correlation. The high dimensionalityof spectral data requires statistical filtering to build meaningfulmodels. For example, the acoustically smoothed signal may be sampled at512 linearly spaced frequencies, and each value may be averaged acrossreplicates and used to create a statistical model. According to apreferred exemplary embodiment, the dimensionality regression modulereduces the total frequencies of the spectral data to a reasonablyacceptable number for model development with high correlation. Accordingto another preferred exemplary embodiment, dimensionality reduction ofthe frequencies for variable selection is done using n the foregoingexample, the total frequencies may be reduced from 512 to 18.

The data from the dimensionality regression module (0901) may beprocessed with a Variance inflation factors module (VIF) (0902). The VIFmodule measures how much the variance of the estimated regressioncoefficients are inflated as compared to when the predictor variablesare not linearly related. The VIF is used to describe how muchmulticollinearity (correlation between predictors) exists in aregression analysis. As it is known, Multicollinearity is problematicbecause it can increase the variance of the regression coefficients,making them unstable and difficult to interpret. The square root of thevariance inflation factor indicates how much larger the standard erroris, compared with what it would be if that variable were uncorrelatedwith the other predictor variables in the model. For Example, if thevariance inflation factor of a predictor variable were 5.27 (√5.27=2.3)this means that the standard error for the coefficient of that predictorvariable is 2.3 times as large as it would be if that predictor variablewere uncorrelated with the other predictor variables.

The data from variance inflation factors module (VIF) (0902) may furtherbe processed with a principal component analysis module (0903).Principal component analysis (PCA) is a technique used to emphasizevariation and bring out strong patterns in a dataset. It's often used tomake data easy to explore and visualize. As defined in the art,Principal component analysis (PCA) is a statistical procedure that usesan orthogonal transformation to convert a set of observations ofpossibly correlated variables into a set of values of linearlyuncorrelated variables called principal components. The number ofprincipal components is less than or equal to the number of originalvariables. This transformation is defined in such a way that the firstprincipal component has the largest possible variance (that is, accountsfor as much of the variability in the data as possible), and eachsucceeding component in turn has the highest variance possible under theconstraint that it is orthogonal to (i.e., uncorrelated with) thepreceding components. According to a preferred exemplary embodiment, aprincipal components analysis is used to determine most relevantfrequencies in the acoustic signal for developing a quantitativeacoustic texture model. It should be noted that any other analysistechnique known in the art may be used to identify principal componentssuch as the relevant frequencies.

The data from the PCA module (0903) is further regressed with a bestsubsets regression module (0904) which is used to determine which ofthese most relevant frequencies are best for texture attribute modelbuilding with good correlation. An R² value greater than 0.9 may beconsidered a good correlation between the measure value from the modeland descriptive expert panel number.

Exemplary Texture Attribute Measurement Method (1000)

As generally shown in FIG. 10, an exemplary texture measurement methodmay be generally described in terms of the following steps:

-   -   (1) striking a surface of a food product with a laser while the        food product is moving on a production line, thereby generating        an acoustic signal from the surface of the food product (1001);    -   (2) capturing the acoustic signal with an acoustic capturing        device (1002);    -   (3) sending the acoustic signal to a data processing unit        coupled to the acoustic capturing device (1003);    -   (4) converting the acoustic signal from a time domain to a        frequency domain (1004); acoustic signal is captured for a        period of time and the signal is plotted as Intensity (dB) vs.        Time (seconds)    -   (5) identifying relevant frequencies and their associated        intensities (1005); and    -   (6) quantifying the texture attribute of the food product based        on said relevant frequencies and said associated intensities        (1006).

This general method summary may be augmented by the various elementsdescribed herein to produce a wide variety of invention embodimentsconsistent with this overall design description.

Exemplary Texture Attribute Correlation Method (1100)

As generally shown in FIG. 11, an exemplary texture correlation methodmay be generally described in terms of the following steps:

-   -   (1) shipping food snack samples to an expert panel (1101);    -    The shipping of the food snack samples may take time and the        food snack may undergo texture change during the shipping        process. The number of times samples are shipped to an expert        panel is substantially reduced due a high correlation model        developed according to a preferred exemplary embodiment.    -   (2) Qualitatively analyzing the food snack samples (1102);    -    quantitatively measure texture attributes by an expert panel        for assigning taste panel scores.    -   (3) Assigning a descriptive panel number for the texture        attributes of the food snack sample (1103);    -   (4) Measuring texture attributes using an non-invasive acoustic        analytical method (1104);    -   (5) Correlating the analytical and the qualitative texture        attributes (1105); and    -   (6) Generating a correlation model for the texture attributes        (1106). The adjusted R² of the correlation is targeted to be        greater than 0.9.

This general method summary may be augmented by the various elementsdescribed herein to produce a wide variety of invention embodimentsconsistent with this overall design description.

Exemplary Texture Attribute Model Development Method (1200)

As generally shown in FIG. 12, an exemplary texture attribute modeldevelopment method may be generally described in terms of the followingsteps:

-   -   (1) Receiving a raw acoustic signal (1201);    -   (2) Filtering, smoothing and transforming the raw acoustic        signal (1202);    -    The signal may be adjusted for background noise. For example an        empty cell may be used to capture background frequencies that        may be compensated by addition or deletion in the captured        acoustic signal. The background noise may be compensated for        frequencies below 20 KHz and may not be compensated for        frequencies above 20 KHz.    -   (3) Regressing and identifying relevant frequencies (1203);    -   (4) Generating a model for the texture attributes (1204).

This general method summary may be augmented by the various elementsdescribed herein to produce a wide variety of invention embodimentsconsistent with this overall design description.

It should be noted that the method used to generate the aforementionedtexture attribute model may be used to generate models for other foodproperties such a moisture, solids content, oil content, slicethickness, density, blister density and topical seasonings. Anyparticles in the seasonings with a particle size of 100 microns to 500microns may be measured with a model using the non-destructive photoacoustic method. A concentration by weight of the seasonings may becalculated from the particle size. For example, a concentration of aseasoning such as sodium chloride may be measured with a model developedwith the photo acoustic method as aforementioned in FIG. 12. Therelevant frequencies and associated intensities and the coefficients ofthe developed model may change depending on the food property that ismeasured with the photo acoustic method.

Exemplary Acoustic Photo Acoustic Signal Generation Method (1300)

As generally shown in FIG. 13, an exemplary Photo Acoustic SignalGeneration method may be generally described in terms of the followingsteps:

(1) Creating small region of highly-heated material in a food snack(1301);

(2) Expanding the material rapidly (1302);

(3) Creating pressure waves from the material (1303);

(4) Propagating the pressure waves through the air as sound (1304).

This general method summary may be augmented by the various elementsdescribed herein to produce a wide variety of invention embodimentsconsistent with this overall design description.

The acoustic model may be developed using the method described in FIG. 9(0900). The model may be programmed into the tool (1306) for measuringone or more texture attributes such as hardness, fracturability anddenseness. An acoustic model for texture attribute hardness may bedescribed below:Hardness=f(X _(1-n) ,I _(1-n))Hardness=I ₁ C ₁ +I ₂ C ₂ +I ₃ C ₃ + . . . I _(n) C _(n)  (1)

Where, I_(n) is an intensity associated with a frequency X_(n)

C_(n) is a coefficient associated with the frequency X_(n)

Coefficients (C₁-Cn) are determined using the energy excitation methoddescribed in FIG. 9 (0900). A signal processing unit in the texturemeasurement tool (1306) identifies the relevant frequencies (X_(n)) andassociated intensities (I_(n)). The tool (1306) may calculate a textureattribute such as hardness from the above model 1 by substituting thecoefficients values (C₁-Cn) from a stored table for the food snack andthe intensities (I_(n)) from the processed acoustic signal. Similarly,other texture attribute such as fracturability and denseness may becalculated from their respective models comprising the respectivecoefficients. It should be noted that even though the above representedmodel (1) shows a linear relationship between the texture attribute andintensities, a quadratic or polynomial model may also be represented tocalculate the texture attributes. The hardness may also be compensatedfor changes in temperature of the food snack and the distance of thefood snack from the focal point of the laser beam.

Similar acoustic models may be developed for models for other foodproperties such a moisture, solids content, oil content, slicethickness, density, blister density and topical seasonings. The relevantfrequencies and associated intensities and the coefficients of thedeveloped model may change depending on the food property. A genericmodel that may represent a food property may be described below:Food property=f(Z _(1-n) ,P _(1-n))Food Property=P ₁ D ₁ +P ₂ D ₂ +P ₃ D ₃ + . . . P _(n) D _(n)  (2)

Where, I_(n) is an intensity associated with a frequency X_(n)

C_(n) is a coefficient associated with the frequency X_(n)

Coefficients (D₁-Dn) are determined using the energy excitation methoddescribed in FIG. 9 (0900). A signal processing unit in the texturemeasurement tool (1306) identifies the relevant frequencies (Z_(n)) andassociated intensities (P_(n)). In addition to texture attribute, thetool (1306) may calculate a food property from the above model (2) bysubstituting the coefficients values (D₁-Dn) from a stored table for thefood snack and the intensities (P_(n)) from the processed acousticsignal. The food properties may include Solids content, Moisture,Density, Oil content, Slice thickness, Seasoning particle size, andelements such as sodium, calcium, copper, zinc, magnesium, andpotassium.It should be noted that even though the above represented model (1)shows a linear relationship between the texture attribute andintensities, a quadratic or polynomial model may also be represented tocalculate the texture attributes. The food property may also becompensated for changes in temperature of the food snack and thedistance of the food snack from the focal point of the laser beam. Atable 1.0 may be used to measure food properties as shown below from acaptured and processed acoustic signal. The values shown below in table1.0 are for illustration purposes only and should not be construed as alimitation.

TABLE 1.0 Relevant Inten- Coeffi- Food Frequen- sities cients Propertycies (Z_(n)) (P_(n)) (D_(n)) Value Limits Texture  14000 Hz 68 3.5 7 4to 10 Attribute  15000 Hz 71 2.3 Solids  16000 Hz 75 1.1 17 12 to 25content 33,000 Hz 77 9.0 Density  88000 Hz 83 8.2 1.3 1 to 12 Oilcontent  16000 Hz 59 2.5 36% 20% to 49,000 Hz 70 2.9 46% Slice  76000 Hz64 4.3 0.055 0.035 to thickness 0.075 Seasoning  64000 Hz 74 8.8   0.5%0.1% to particle size 15% Element  97000 Hz 82 3.7 Na Can be (sodium)any listed element

In a manufacturing process, as the food snacks on a conveyor belt passfrom a processing unit to a seasoning station, the excitation tool in ameasurement tool placed in line may strike the food snack repeatedly fora set period of time. According to a preferred exemplary embodiment, theexcitation tool may continuously strike the food snack for a period of 1micro second. According to a yet another preferred exemplary embodiment,the excitation tool may continuously strike the food snack for a periodof 1 second. According to a more preferred exemplary embodiment, theexcitation tool may continuously strike the food snack for a period of 1micro second to 10 seconds. According to a most preferred exemplaryembodiment, the excitation tool may continuously strike the food snackfor a period of 13 seconds. The excitation tool may strike a particularfood snack on the conveyor belt repeatedly so that multiple acousticsignals are generated for the entire surface of the food snack. It isknown that the texture attribute may not be uniform across the entiresurface. The excitation energy may strike the food snack across theentire area of the food snack so that any imperfections such as blistersmay be detected after the signal has been processed. According to apreferred exemplary embodiment, repeatable measurements for a period oftime, enables the measurement tool to identify subtle variations acrossthe entire surface of a food snack. The signal may be captured/recordedby an acoustic capturing device in the texture measurement tool.

The acoustic capturing device may capture the acoustic signal across awide range of frequencies. Additionally, the acoustic capturing devicemay be placed an angle directly above the food snack. According to apreferred exemplary embodiment, the acoustic capturing device capturesacoustic signals in a unidirectional manner. According to anotherpreferred exemplary embodiment, the acoustic capturing device capturesacoustic signals in an omnidirectional manner. The acoustic capturingdevice may forward the captured acoustic signal to a processing devicephysically through a cable. According to a preferred exemplaryembodiment, the acoustic capturing device is a wireless microphone thatcontains a radio transmitter. In a preferred exemplary embodiment, theacoustic capturing device is a dynamic microphone. In another preferredexemplary embodiment, the acoustic capturing device is a fiber opticmicrophone. A fiber optic microphone converts acoustic waves intoelectrical signals by sensing changes in light intensity, instead ofsensing changes in capacitance or magnetic fields as with conventionalmicrophones. The acoustic capturing device may use electromagneticinduction (dynamic microphones), capacitance change (condensermicrophones) or piezoelectricity (piezoelectric microphones) to producean electrical signal from air pressure variations. The microphones maybe connected to a preamplifier before the signal can be amplified withan audio power amplifier or recorded. The microphones may be regularlycalibrated due to the sensitivity of the measurement. In anotherpreferred exemplary embodiment, the acoustic capturing device has adigital interface that directly outputs a digital audio stream throughan XLR or XLD male connector. The digital audio stream may be processedfurther without significant signal loss. According to a preferredexemplary embodiment the acoustic capturing device may be a hydrophone.The hydrophone may be in communication with a data processing unit. Thehydrophone may be used in fluid environments.

Exemplary Acoustic Signal Processing Method (1400)

As generally shown in FIG. 14, an exemplary Photo Acoustic SignalProcessing method may be generally described in terms of the followingsteps:

-   -   (1) Receiving an raw acoustic signal (1401);    -   (2) Smoothing the raw acoustic signal with a windowing function        to create a smoothened acoustic signal (1402);    -   (3) Transforming the smoothened acoustic signal into a frequency        domain signal (1403);    -   (4) Increasing the signal-to-noise of the frequency domain        signal (1404); and    -   (5) Normalizing and bucketing the frequency domain signal        (1405).

This general method summary may be augmented by the various elementsdescribed herein to produce a wide variety of invention embodimentsconsistent with this overall design description.

Exemplary Acoustic Statistical Processing Method (1500)

As generally shown in FIG. 15, an exemplary statistical processingmethod may be generally described in terms of the following steps:

-   -   (1) Receiving a frequency domain acoustic signal (1501);    -   (2) Selecting variables based on dimensionality reduction of the        frequencies in the frequency domain acoustic signal (1502);    -   (3) Filtering selected variables with a principal component        analysis (1503);    -   (4) Performing subset regression of the filtered variables        (1504); and    -   (5) Generate a model of texture attributes with the filtered        variables (1505).    -    The filtered variables may be the relevant frequencies in the        acoustic signal that show a strong correlation. (Adjusted        R²>0.9)

This general method summary may be augmented by the various elementsdescribed herein to produce a wide variety of invention embodimentsconsistent with this overall design description.

Exemplary Food Snack Finger Printing Method (1600)

As generally shown in FIG. 16, an exemplary food snack finger printingmethod may be generally described in terms of the following steps:

-   -   (1) Striking a food snack with energy from an energy excitation        tool (1601);    -   (2) generating an acoustic signal from the food snack (1602);    -   (3) capturing the acoustic signal with an acoustic capturing        device (1603);    -   (4) forwarding the acoustic signal to a data matching unit        (1604);    -   (5) measuring a food property number of the food snack with an        photo acoustic model (1605);    -   (6) comparing the food property number with an entry in a        matching table (1606);    -   (7) if a match exists in step (1606), finger printing the food        snack (1607); and    -   (8) if a match does not exist in step (1606), adding the food        snack to the database for further use (1608).

This general method summary may be augmented by the various elementsdescribed herein to produce a wide variety of invention embodimentsconsistent with this overall design description.

Exemplary Food Property Matching Table (1700)

As generally illustrated in FIG. 17, an exemplary food property matchingtable (1700) is shown. The table may include a food snack in column(1701) and an associated food property (1702) in another column. Theentries (1711, 1712) may include data for the food snack and foodproperty respectively and the entries may be used for matching purposes.For example, food snack column (1701) may comprise various solids andtheir associated texture in column (1702). Each of the entries in thetable (1700) may be populated after a photo acoustic model for the foodsnack has been developed by the aforementioned methods described in FIG.12 (1200). For example, an entry (1711), may be a potato chip A. A rangefor the texture or other food properties may be determined with thephoto acoustic model for the potato chip A and entered as an entry intable (1700). Similarly, food properties for other food products aremeasured with the photo acoustic model and entered into the table. Thephoto acoustic model may or may not be correlated with an expert panelnumber. The food property number may be a single texture attribute, acombination of texture attributes or a composite number comprising acombination of other food properties such as moisture, oil content,slice thickness, brittleness, solids content and so on hen a food snackis measured with a photo acoustic measurement method a food propertynumber may be determined. The food property number may be obtained froma single sample or an average of multiple samples. The measured foodproperty number may then be looked up in the column (1702) in thematching table (1700) and a corresponding food snack is determined inthe column (1701). Thereby, a food snack is finger printed based onphoto acoustic measurement. According to an exemplary embodiment, foodsnacks with subtle differences in food property may be differentiatedwith the food finger printing technique. For examples, various potatochips such as baked, fried, and/or textured may be differentiated bymeasuring each of them and looking up the corresponding potato chip inthe matching table (1700) from the measured food property numbers. Foodsmay be separated into buckets with the photo acoustic measurement andmatching process as aforementioned in FIG. 16 (1600).

Exemplary Acoustic Signal Time Domain to Frequency Domain Conversion(1800)

As generally illustrated in FIG. 18, an exemplary acoustic signalcaptured in time domain (transient) (1810) is converted to a frequencydomain (1820) with Fourier transformation. When an electromagnetic wavesuch as a laser strikes a food snack, an acoustic signal is captured intime domain and is recorded and plotted as Intensity (dB) vs. Time(secs). The recorded acoustic signal may be transformed into a frequencydomain signal as illustrated in FIG. 18 (1820). The transformed acousticsignal may be further processed to identify relevant frequencies basedon a statistical regression analysis. An acoustic model toquantitatively measure a texture attribute may be developed with theidentified relevant frequencies and their associated intensities asvariables.

Exemplary Texture Attribute vs. Relevant Frequencies Chart (1900-2100)

As generally illustrated in FIG. 19 and FIG. 20, an exemplary textureattribute Intensity vs. relevant frequencies chart may be used tocompute the texture attribute of a food snack. The relevant frequenciesmay be identified by a statistical regression for a particular textureattribute and a food snack. For example, frequencies (1901) may berelevant for hardness and frequencies (2001) may be relevant forfracturability as determined by a statistical analysis described in FIG.9 (0900). According to a preferred exemplary embodiment, the relevantfrequencies and corresponding intensities identified in a transformedacoustic signal may be substituted in an acoustic model toquantitatively measure a texture attribute such as hardness. It shouldbe noted that the frequencies indicated on x-axis are frequency“buckets” as determined by an algorithm, and not the literal frequencies(i.e. 400 may not be 400 Hz, but more like 18,000 hz).

As generally illustrated in FIG. 21, an exemplary texture attributeIntensity (dB) (2101) vs. relevant frequencies (2102) chart for a foodsnack treated with various input conditions. Plot (2114), (2115), (2116)are frequency vs Intensity graphs for a potato chip with different solidcontent, moisture content and hardness of the input ingredients such aspotatoes. For example, a plot (2114) may be a frequency vs intensityplot for a food snack that has a different solids content in the inputingredients. Similarly, a plot (2115) may be a frequency vs intensityplot for a food snack that has a different moisture content anddifferent hardness in the input ingredients respectively. A plot (2106)may be plotted for background noise so that the resulting plot may becompensated for the noise. After identifying the relevant frequenciesfor a food snack such as a potato chip, an acoustic signal may becaptured for each of the input conditions and the acoustic signal may befurther processed to determine the intensities associated with theidentified frequencies for the food property of the food snack. Forexample in FIG. 21, an identified frequency 40000 Hz may have anintensity of 75 dB (2103) for plot (2113), an intensity of 74 dB (2104)for plot (2114) and an intensity of 76 dB (2105) for plot (2115). Theintensities may be substituted into a food property model generated byaforementioned equation (2) and a food property such as a textureattribute may be calculated. As illustrated in FIG. 21, the 3 differentinput conditions of the food ingredients (solids content, moisturecontent and hardness) resulted in 3 different associated intensitieswhich further result in 3 different texture attributes. Therefore, anacoustic signal may be captured and processed for a food product and atexture attribute may be calculated based on the relevant frequencies.The input conditions may be tailored to achieve a desirable textureattribute value that is within a predefined limit. The predefined limitmay be correlated to a qualitative descriptive panel number. Similarly,plots may be generated for various food properties by capturing anacoustic signal and processing it. The intensities associated with thevarious food properties at their respective frequencies may bedetermined and the food property may be calculated. A model may begenerated for each of the food properties through signal processing andstatistical regression as aforementioned. Therefore, a photo acousticmethod may be used to identify differences in a food product based onany food property such as a texture attribute, solids content, moisture,oil content, density, blister density and elements such as Sodium,Potassium, Calcium, and Magnesium. The differences in the food productmay be as minor as +−5% of the desirable value. For example, a desirablehardness value of 75 may be differentiated from a hardness value of 70that may be undesirable for the food product. The food product with theundesirable value (70) may be rejected and not further processed orpackaged.

System Summary

The present invention system anticipates a wide variety of variations inthe basic theme of texture measurement apparatus that includes an energyexcitation tool, an acoustic capturing device, and a data processingunit. The energy excitation tool directs a laser towards a food snackplaced on a surface and creates pressure waves that propagate throughthe air and produce an acoustic signal. The acoustic capturing devicerecords and forwards the signal to a data processing unit. The dataprocessing unit further comprises a digital signal processing modulethat smoothens, transforms and filters the received acoustic signal. Astatistical processing module further filters the acoustic signal fromthe data processing unit and generates a quantitative acoustic model fortexture attributes such as hardness and fracturability. The quantitativemodel is correlated with a qualitative texture measurement from adescriptive expert panel. Texture of food snacks are quantitativelymeasured with the quantitative acoustic model.

This general system summary may be augmented by the various elementsdescribed herein to produce a wide variety of invention embodimentsconsistent with this overall design description.

Method Summary

The present invention method anticipates a wide variety of variations inthe basic theme of implementation, but can be generalized as aquantitative method for measuring texture attribute of a food snack, themethod comprises the steps of:

-   -   a) striking a surface of a food product with a laser while the        food product is moving on a production line, thereby generating        an acoustic signal from the surface of the food product;    -   b) capturing the acoustic signal with an acoustic capturing        device;    -   c) sending the acoustic signal to a data processing unit coupled        to the acoustic capturing device;    -   d) converting the acoustic signal from a time domain to a        frequency domain;    -   e) identifying relevant frequencies and their associated        intensities; and    -   f) quantifying the texture attribute of the food product based        on the relevant frequencies and the associated intensities.

This general method summary may be augmented by the various elementsdescribed herein to produce a wide variety of invention embodimentsconsistent with this overall design description.

System/Method Variations

The present invention anticipates a wide variety of variations in thebasic theme of a quantitative texture measurement. The examplespresented previously do not represent the entire scope of possibleusages. They are meant to cite a few of the almost limitlesspossibilities.

This basic system and method may be augmented with a variety ofancillary embodiments, including but not limited to:

-   -   An embodiment wherein shape of the housing is a hollow cylinder.    -   An embodiment wherein shape of the housing is a parabolic dish.    -   An embodiment wherein the food product is placed on a stationary        surface when the laser strikes the food product.    -   An embodiment wherein the food product is passing within the        housing when the laser strikes the food product.    -   An embodiment wherein the acoustic capturing device is        configured to capture frequencies in the acoustic signal; the        frequencies range from 0 to 200 KHz.    -   An embodiment wherein a distance between the acoustic capturing        device and the product ranges from 2 inch to 2 feet.    -   An embodiment wherein the acoustic capturing device is        positioned such that the acoustic capturing device is configured        to capture energy density in the acoustic signal within a range        of 62.5 mJ/mm² to 594.5 mJ/mm².    -   An embodiment wherein the food product is a starch based food        snack.    -   An embodiment wherein the food product is a potato chip.    -   An embodiment wherein the data processing unit further comprises        a digital signal processing unit and a texture attribute        computing unit.    -   An embodiment wherein the digital signal processing unit is        configured to smoothen, transform and filter the acoustic signal        to identify relevant frequencies relating to the texture        attribute.    -   An embodiment wherein the texture attribute computing unit is        configured to calculate the texture attribute from the        frequencies captured in the acoustic signal.    -   An embodiment wherein the texture attribute is selected from a        group comprising: hardness, fracturability, tooth-pack,        crispiness, denseness, roughness of mass, moistness of mass,        residual greasiness, surface roughness, and surface oiliness.    -   An embodiment wherein the food snack remains intact after the        strike from the excitation tool.    -   An embodiment wherein the acoustic capturing device is a        microphone; the microphone is configured to be wired to the data        processing unit.    -   An embodiment wherein the acoustic capturing device is a        microphone; the microphone is configured to wirelessly connect        with the data processing unit. An embodiment wherein the        acoustic capturing device is configured to capture the acoustic        signal in a single direction.    -   An embodiment wherein the acoustic capturing device is        configured to capture the acoustic signal in a plurality of        directions.    -   An embodiment wherein the acoustic capturing device is        integrated with the digital signal processing unit.    -   An embodiment wherein the acoustic capturing device and the data        processing unit are integrated into one unit    -   An embodiment wherein the food product is moving on a production        line when the laser strikes the food product.    -   An embodiment wherein the food product is stationary when the        laser strikes the food product.    -   An embodiment wherein the laser strikes the food product at        multiple locations of the food product.    -   An embodiment wherein the acoustic capturing device captures the        acoustic signal for a period of 1 second to 5 minutes.    -   An embodiment wherein the acoustic capturing device transmits        the acoustic signal wirelessly to the data processing unit.    -   An embodiment wherein the laser strikes the food product        continuously for a period of 1 micro second to 10 seconds.    -   An embodiment wherein the food product is a starch based food        snack.    -   An embodiment wherein a diameter of the laser beam ranges from        200 micrometer to 400 micrometers.    -   An embodiment wherein the texture attribute is selected from a        group comprising: hardness, fracturability, tooth-pack,        crispiness, denseness, roughness of mass, moistness of mass,        residual greasiness, surface roughness, and surface oiliness.

One skilled in the art will recognize that other embodiments arepossible based on combinations of elements taught within the aboveinvention description.

What is claimed is:
 1. An apparatus for quantitative non-destructivetexture attribute measurement of a food product, comprising; a housing;a laser generator attached to said housing; an acoustic capturing deviceproximally located to said housing; a data processing unit incommunication with at least said acoustic capturing device; wherein alaser from said laser generator is directed to strike said food product,thereby producing an acoustic signal to be detected by said acousticcapturing device; wherein further said data processing unit isconfigured to quantitatively measure said texture attribute of said foodproduct based on input from said acoustic capturing device and saidtexture attribute is selected from a group comprising: hardness,fracturability, tooth-pack, crispiness, denseness, roughness of mass,moistness of mass, residual greasiness, surface roughness, or surfaceoiliness.
 2. The apparatus of claim 1, wherein shape of said housing isa hollow cylinder.
 3. The apparatus of claim 1, wherein shape of saidhousing is a parabolic dish.
 4. The apparatus of claim 1, wherein saidfood product is placed on a stationary surface when said laser strikessaid food product.
 5. The apparatus of claim 1, wherein said foodproduct is passing within said housing when said laser strikes said foodproduct.
 6. The apparatus of claim 1, wherein said acoustic capturingdevice is configured to capture frequencies in said acoustic signal;said frequencies range from 0 to 200 KHz.
 7. The apparatus of claim 1,wherein a distance between said acoustic capturing device and saidproduct ranges from 2 inch to 2 feet.
 8. The apparatus of claim 1,wherein said acoustic capturing device is positioned such that saidacoustic capturing device is configured to capture energy density insaid acoustic signal within a range of 62.5 mJ/mm² to 594.5 mJ/mm². 9.The apparatus of claim 1, wherein said food product is a starch basedfood snack.
 10. The apparatus of claim 1, wherein said food product is apotato chip.
 11. The system of claim 1, wherein said data processingunit further comprises a digital signal processing unit and a textureattribute computing unit.
 12. The system of claim 11, wherein saiddigital signal processing unit is configured to smoothen, transform andfilter said acoustic signal to identify relevant frequencies relating tosaid texture attribute.
 13. The system of claim 11, wherein said textureattribute computing unit is configured to calculate said textureattribute from said frequencies captured in said acoustic signal. 14.The system of claim 1, wherein said food snack remains intact after saidstrike from said laser generator.
 15. The system of claim 1 wherein saidacoustic capturing device is a microphone; said microphone is configuredto be wired to said data processing unit.
 16. The system of claim 1wherein said acoustic capturing device is a microphone; said microphoneis configured to wirelessly connect with said data processing unit. 17.The system of claim 1 wherein said acoustic capturing device isconfigured to capture said acoustic signal in a single direction. 18.The system of claim 1 wherein said acoustic capturing device isconfigured to capture said acoustic signal in a plurality of directions.19. The system of claim 11 wherein said acoustic capturing device isintegrated with said digital signal processing unit.
 20. The system ofclaim 1 wherein said acoustic capturing device and said data processingunit are integrated into one unit.
 21. A non-destructive photo acousticquantitative method for measuring texture attribute of a food product,wherein said texture attribute is selected from a group comprising:hardness, fracturability, tooth-pack, crispiness, denseness, roughnessof mass, moistness of mass, residual greasiness, surface roughness, orsurface oiliness, said method comprises the steps of: a) striking asurface of a food product with a laser, thereby generating an acousticsignal from a surface of said food product; b) capturing said acousticsignal with an acoustic capturing device; c) sending said acousticsignal to a data processing unit coupled to said acoustic capturingdevice; d) converting said acoustic signal from a time domain to afrequency domain; e) identifying relevant frequencies and theirassociated intensities; and f) quantifying said texture attribute ofsaid food product based on said relevant frequencies and said associatedintensities.
 22. The method of claim 21 wherein said food product ismoving on a production line when said laser strikes said food product.23. The method of claim 21 wherein said food product is stationary whensaid laser strikes said food product.
 24. The method of claim 21 whereinsaid laser strikes said food product at multiple locations of said foodproduct.
 25. The method of claim 21, wherein said acoustic capturingdevice captures said acoustic signal for a period of 1 second to 5minutes.
 26. The method of claim 21 wherein said laser strikes said foodproduct continuously for a period of 1 micro second to 10 seconds. 27.The method of claim 21, wherein said food product is a starch based foodsnack.
 28. The method of claim 21, wherein a diameter of said laser beamranges from 200 micrometers to 400 micrometers.
 29. An apparatus forquantitative non-destructive texture attribute measurement of a foodproduct, comprising; a housing; a laser generator attached to saidhousing; an acoustic capturing device proximally located to saidhousing; a data processing unit in communication with at least saidacoustic capturing device; wherein a laser from said laser generator isdirected to strike said food product, thereby producing an acousticsignal to be detected by said acoustic capturing device; wherein furthersaid data processing unit is configured to quantitatively measure saidtexture attribute of said food product based on input from said acousticcapturing device; and said acoustic capturing device is positioned suchthat said acoustic capturing device is configured to capture energydensity in said acoustic signal within a range of 62.5 mJ/mm² to 594.5mJ/mm².
 30. The apparatus of claim 29, wherein shape of said housing isa hollow cylinder.
 31. The apparatus of claim 29, wherein shape of saidhousing is a parabolic dish.
 32. The apparatus of claim 29, wherein saidfood product is placed on a stationary surface when said laser strikessaid food product.
 33. The apparatus of claim 29, wherein said foodproduct is passing within said housing when said laser strikes said foodproduct.
 34. The apparatus of claim 29, wherein said acoustic capturingdevice is configured to capture frequencies in said acoustic signal;said frequencies range from 0 to 200 KHz.
 35. The apparatus of claim 29,wherein a distance between said acoustic capturing device and saidproduct ranges from 2 inch to 2 feet.
 36. The apparatus of claim 29,wherein said food product is a starch based food snack.
 37. Theapparatus of claim 29, wherein said food product is a potato chip. 38.The system of claim 29, wherein said data processing unit furthercomprises a digital signal processing unit and a texture attributecomputing unit.
 39. The system of claim 38, wherein said digital signalprocessing unit is configured to smoothen, transform and filter saidacoustic signal to identify relevant frequencies relating to saidtexture attribute.
 40. The system of claim 38, wherein said textureattribute computing unit is configured to calculate said textureattribute from said frequencies captured in said acoustic signal. 41.The system of claim 29, wherein said food snack remains intact aftersaid strike from said laser generator.
 42. The system of claim 29wherein said acoustic capturing device is a microphone; said microphoneis configured to be wired to said data processing unit.
 43. The systemof claim 29 wherein said acoustic capturing device is a microphone; saidmicrophone is configured to wirelessly connect with said data processingunit.
 44. The system of claim 29 wherein said acoustic capturing deviceis configured to capture said acoustic signal in a single direction. 45.The system of claim 29 wherein said acoustic capturing device isconfigured to capture said acoustic signal in a plurality of directions.46. The system of claim 38 wherein said acoustic capturing device isintegrated with said digital signal processing unit.
 47. The system ofclaim 29 wherein said acoustic capturing device and said data processingunit are integrated into one unit.